Bias and variance are two major issues we try to manage in machine learning. We are always looking for a trade-off between the two. Bias has to do with the training data while variance has to do with the model output.

Developers who are new to programming can start with Python by joining Python training classes and then move to other languages. Python’s object-oriented principles are compatible with other languages like JavaScript, Perl, Ruby, and C#.

This paper identifies a gap in the application of machine learning in the industry, especially petroleum. Recent development has led to massive employment of data scientists who know little about the problem domain. It is recommended that data scientists work closely with domain experts to build robust solutions rather than working in isolation.

Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.